Training course · Windsurf · API Development

Windsurf for API Developers

Develop with the Windsurf AI IDE and its Cascade agent: flows, memories, rules, workflows and safe use across the team.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Windsurf with the everyday processes of API Development. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Windsurf is an AI-powered development environment built around its agent, Cascade. Cascade understands the structure of your project, follows your actions in the editor and terminal, and works with you according to the flow principle: you and the agent take turns on the same task without having to re-explain context over and over. In this two-day course you will learn how to use Cascade effectively for features, debugging and refactoring, and how to make the most of intelligent completion in your daily development work.

To keep results consistent across a team, you steer Cascade with global and workspace rules, rely on memories for lasting project knowledge and automate recurring procedures with workflows that run from a slash command. You will connect external tools through MCP, control how terminal commands are executed and decide which data Cascade is allowed to see. Finally, you will define rules for code review, security and data protection so that Windsurf can be used responsibly within your organisation and its existing processes.

APIs form the backbone of modern software landscapes and are increasingly the interface through which AI agents interact with systems. AI coding assistants speed up the entire API lifecycle, from drafting an OpenAPI or AsyncAPI specification to generating server stubs, clients and contract tests, through to documentation and versioning. At the same time, demands for consistency, security and backward compatibility keep rising, because APIs are consumed by many teams, partners and automated agents. In this course you learn to practise API-first development with AI support, enforce design guidelines automatically, catch breaking changes early and expose existing interfaces to AI agents through the Model Context Protocol or tool definitions.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Backend developers building REST, GraphQL or gRPC interfaces
  • API architects and owners of API governance
  • Integration developers connecting partners and platforms
  • Platform engineering teams running API gateways and developer portals
  • Developers making company data available to AI agents

Typical challenges we address

  • Inconsistent API design across teams with differing conventions for errors, pagination and versioning
  • Specification, implementation and documentation drift apart
  • Breaking changes are only discovered by consumers
  • Securing authentication, authorisation and rate limiting as users and agent traffic grow

What you will be able to do

  • You use Cascade productively in flow for features, debugging and refactoring.
  • You steer Windsurf’s context precisely and keep sensitive content protected.
  • You maintain memories and rules that reliably enforce your project conventions.
  • You automate recurring procedures with workflows and connect external tools via MCP.
  • Practise API-first development with AI support, from specification to clients, tests and documentation.
  • Automate design governance, contract testing and breaking-change detection in your pipeline.
  • Make existing APIs available to AI agents securely and in a controlled way, for example via the Model Context Protocol.

Course outline

Day 1

Module 1 Getting to know Windsurf and Cascade
  • Installation, importing existing editor settings and basic configuration
  • Cascade in code and chat mode: when the agent edits and when it only advises
  • The flow principle: how Cascade takes your editor and terminal actions into account
  • Intelligent completion and inline commands in everyday development
Module 2 Steering context
  • Understanding codebase indexing and referencing relevant files or directories
  • Bringing documentation, web sources and terminal output into context
  • Excluding sensitive content from context with ignore files
  • Structuring sessions and starting afresh when the topic changes
Module 3 Memories and rules for consistent results
  • Reviewing and maintaining auto-generated and manually created memories
  • Creating and versioning global and workspace rules
  • Expressing architecture, style and testing guidelines as effective rules
  • Activating rules selectively by file pattern or occasion
Module 4 Workflows and automation
  • Defining recurring procedures as workflows and launching them via slash command
  • Building workflows for release preparation, testing or code maintenance
  • Connecting MCP servers for issue trackers, databases or internal services
  • Controlling terminal command execution with allow and deny lists

Day 2

Module 5 Quality, review and governance
  • Tracing Cascade’s changes, accepting them partially or reverting them
  • Using a test-driven approach and preview features for verification
  • Team settings, data protection options and handling of secrets
  • Defining review processes and accountability for AI-generated code
Module 6 Practice lab: API Development
  • Generating OpenAPI specs
  • Generating servers and clients
  • Building contract tests
  • Detecting breaking changes
  • Reviewing API security
  • Opening APIs to AI agents
Module 7 Data protection, quality & transfer to daily work
  • APIs frequently carry personal and confidential data: you learn to create specifications and sample data without real customer records, keep API keys and tokens out of the AI context and apply data protection by design under the GDPR as well as NIS2 requirements.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Generating OpenAPI specs

Draft complete OpenAPI specifications with schemas, examples, error objects and security schemes from requirements, domain models or existing code, and check them against your style guide rules.

Generating servers and clients

Derive controller scaffolding, validation logic and type-safe client SDKs for several languages from a specification, keeping generated parts cleanly separated from hand-written business logic. Regenerating after spec changes is simple.

Building contract tests

Define consumer-driven contracts, generate test cases for edge values, error paths and authentication, and run them automatically in CI against both the specification and the live implementation.

Detecting breaking changes

Compare two versions of a specification, identify incompatible changes, write migration notes for consumers and plan deprecation strategies using versioning and sunset headers. The check runs on every pull request.

Reviewing API security

Analyse endpoints for typical weaknesses from the OWASP API Security Top 10, such as broken object-level authorisation, mass assignment or missing rate limits, and propose fixes.

Opening APIs to AI agents

Expose existing interfaces as MCP servers or tool definitions, phrase descriptions and parameters so agents use them correctly and scope permissions so agents can only perform approved actions.

Possible hands-on projects

API linter with house rules

A command-line tool that checks OpenAPI files against your design guidelines and posts clear improvement suggestions directly on the pull request.

Mock server from spec

A small service that generates realistic sample responses from a specification so frontend teams can work in parallel with backend development.

MCP server for an internal API

An MCP server that exposes selected endpoints of an internal REST API as tools for AI assistants, with authentication and logging.

Prerequisites

You program regularly in at least one language and are familiar with Git and a modern IDE. You build backend applications in a mainstream language such as Java, C#, TypeScript, Python or Go and understand HTTP, JSON and REST fundamentals.

Technical requirements

Your own laptop with the Windsurf editor, Git and your usual toolchain installed; a Windsurf account with access to Cascade is required.

Data protection & compliance

APIs frequently carry personal and confidential data: you learn to create specifications and sample data without real customer records, keep API keys and tokens out of the AI context and apply data protection by design under the GDPR as well as NIS2 requirements.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Stuttgart Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Berlin Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Online Request
  • Tue, 2 February 2027 – Wed, 3 February 2027 Munich Request
  • Thu, 18 February 2027 – Fri, 19 February 2027 Online Request

Frequently asked questions

What does Windsurf mean by flows?

Flows describe the close collaboration between you and Cascade. The agent observes what you do in the editor and terminal and takes those actions into account in its next steps. As a result you do not have to keep explaining where you are, and you can move smoothly between doing the work yourself and delegating it to the agent.

How do memories, rules and workflows differ?

Memories store knowledge that Cascade has gathered while working or that you have explicitly recorded. Rules are guidelines you write that shape the agent’s behaviour permanently. Workflows are reusable sequences of steps for recurring tasks, launched with a slash command. Together they keep results consistent across the whole team.

Can I use Windsurf with my existing projects and languages?

Yes. Windsurf is language-agnostic and works with your existing toolchain, including build tools, package managers and test frameworks. Whether you work on back end, front end, data processing or infrastructure code, what matters is giving Cascade good rules and clear context. Each course variant adds the specific focus for your field.

Is spec-first still worth it when AI writes code so quickly?

More than ever. A precise specification is the best context you can give an AI assistant: it pins down data structures, error cases and security requirements, serves as a contract for consumers and underpins your tests. Without one, AI quickly produces inconsistent endpoints. In the course you use the spec as the single source for generation and validation.

What is the difference between a REST API and an MCP server?

A REST API is aimed at developers and applications, whereas an MCP server exposes capabilities in a standardised format for AI assistants and agents, including descriptions the model can interpret. An MCP server is often a thin layer on top of existing APIs. You learn when that step pays off and how to implement it securely.

How much does “Windsurf for API Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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